Computer performance by orders of magnitude

• 5×10⁻¹ Speed of the average human mental calculation for multiplication using pen and paper

• 1 OP/S the speed of the average human addition calculation using pen and paper
• 1 OP/S the speed of Zuse Z1
• 5 OP/S world record for addition set

• 5×10¹ Upper end of serialized human perception computation (light bulbs do not flicker to the human observer)

• 2.2×10² Upper end of serialized human throughput. This is roughly expressed by the lower limit of accurate event placement on small scales of time (The swing of a conductor's arm, the reaction time to lights on a drag strip, etc.)[1]
• 2×10² IBM 602 1946 computer.

• 92×10³ Intel 4004 First commercially available full function CPU on a chip, released in 1971
• 500×10³ Colossus computer vacuum tube supercomputer 1943

• 1×10⁶ Motorola 68000 commercial computing 1979
• 1.2×10⁶ IBM 7030 "Stretch" transistorized supercomputer 1961

• 1×10⁹ ILLIAC IV 1972 supercomputer does first computational fluid dynamics problems
• 1.354×10⁹ Intel Pentium III commercial computing 1999
• 147.6×10⁹ Intel Core i7-980X Extreme Edition commercial computing 2010[2]

• 1.34×10¹² Intel ASCI Red 1997 Supercomputer
• 1.344×10¹² GeForce GTX 480 in 2010 from Nvidia at its peak performance
• 4.64×10¹² Radeon HD 5970 in 2009 from AMD (under ATI branding) at its peak performance
• 5.152×10¹² S2050/S2070 1U GPU Computing System from Nvidia
• 11.3×10¹² GeForce GTX 1080 Ti in 2017
• 13.7×10¹² Radeon RX Vega 64 in 2017
• 15.0×10¹² Nvidia Titan V in 2017
• 80×10¹² IBM Watson[3]
• 170×10¹² Nvidia DGX-1 The initial Pascal based DGX-1 delivered 170 teraflops of half precision processing.[4]
• 478.2×10¹² IBM BlueGene/L 2007 Supercomputer
• 960×10¹² Nvidia DGX-1 The Volta-based upgrade increased calculation power of Nvidia DGX-1 to 960 teraflops.[5]

• 1.026×10¹⁵ IBM Roadrunner 2009 Supercomputer
• 2×10¹⁵ Nvidia DGX-2 a 2 Petaflop Machine Learning system (the newer DGX A100 has 5 Petaflop performance)
• 11.5×10¹⁵ Google TPU pod containing 64 second-generation TPUs, May 2017[6]
• 17.17×10¹⁵ IBM Sequoia's LINPACK performance, June 2013[7]
• 20×10¹⁵ Roughly the hardware-equivalent of the human brain according to Kurzweil. Published in his 1999 book: The Age of Spiritual Machines: When Computers Exceed Human Intelligence[8]
• 33.86×10¹⁵ Tianhe-2's LINPACK performance, June 2013[7]
• 36.8×10¹⁵ Estimated computational power required to simulate a human brain in real time.[9]
• 93.01×10¹⁵ Sunway TaihuLight's LINPACK performance, June 2016[10]
• 143.5×10¹⁵ Summit's LINPACK performance, November 2018[11]

• 1×10¹⁸ The U.S. Department of Energy and NSA estimated in 2008 that they would need exascale computing around 2018[12]
• 1×10¹⁸ Fugaku 2020 supercomputer in single precision mode[13]
• 1.88×10¹⁸ U.S. Summit achieves a peak throughput of this many operations per second, whilst analysing genomic data using a mixture of numerical precisions.[14]
• 2.43×10¹⁸ Folding@home distributed computing system during COVID-19 pandemic response[15]

• 1×10²¹ Accurate global weather estimation on the scale of approximately 2 weeks.[16] Assuming Moore's law remains constant, such systems may be feasible around 2030.

A zettascale computer system could generate more single floating point data in one second than was stored by any digital means on Earth in first quarter 2011.

• 4×10⁴⁸ Estimated computational power of a Matrioshka brain, where the power source is the Sun, the outermost layer operates at 10 kelvins, and the constituent parts operate at or near the Landauer limit and draws power at the efficiency of a Carnot engine. Approximate maximum computational power for a Kardashev 2 civilization.
• 5×10⁵⁸ Estimated power of a galaxy equivalent in luminosity to the Milky Way converted into Matrioshka brains. Approximate maximum computational power for a Type III civilization on the Kardashev scale.

• Futures studies – study of possible, probable, and preferable futures, including making projections of future technological advances
• History of computing hardware (1960s–present)
• List of emerging technologies – new fields of technology, typically on the cutting edge. Examples include genetics, robotics, and nanotechnology (GNR).
  • Artificial intelligence – computer mental abilities, especially those that previously belonged only to humans, such as speech recognition, natural language generation, etc.
    • History of artificial intelligence (AI)
    • Strong AI – hypothetical AI as smart as a human. Such an entity would likely be recursive, that is, capable of improving its own design, which could lead to the rapid development of a superintelligence.
  • Quantum computing
    • Timeline of quantum computing
• Moore's law – observation (not actually a law) that, over the history of computing hardware, the number of transistors on integrated circuits doubles approximately every two years. The law is named after Intel co-founder Gordon E. Moore, who described the trend in his 1965 paper.[17]
• Supercomputer
  • History of supercomputing
• Superintelligence
• Timeline of computing
• Technological singularity – hypothetical point in the future when computer capacity rivals that of a human brain, enabling the development of strong AI — artificial intelligence at least as smart as a human.
  • The Singularity is Near – book by Raymond Kurzweil dealing with the progression and projections of development of computer capabilities, including beyond human levels of performance.
• TOP500 – list of the 500 most powerful (non-distributed) computer systems in the world

• Historical and projected growth in supercomputer capacity